Predictability of evolutionary trajectories in fitness landscapes.

Experimental studies on enzyme evolution show that only a small fraction of all possible mutation trajectories are accessible to evolution. However, these experiments deal with individual enzymes and explore a tiny part of the fitness landscape. We report an exhaustive analysis of fitness landscapes...

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Autores principales:	Alexander E Lobkovsky, Yuri I Wolf, Eugene V Koonin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4a480d0839954830b8e1433829248b14
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spelling	oai:doaj.org-article:4a480d0839954830b8e1433829248b142021-11-18T05:51:44ZPredictability of evolutionary trajectories in fitness landscapes.1553-734X1553-735810.1371/journal.pcbi.1002302https://doaj.org/article/4a480d0839954830b8e1433829248b142011-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22194675/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Experimental studies on enzyme evolution show that only a small fraction of all possible mutation trajectories are accessible to evolution. However, these experiments deal with individual enzymes and explore a tiny part of the fitness landscape. We report an exhaustive analysis of fitness landscapes constructed with an off-lattice model of protein folding where fitness is equated with robustness to misfolding. This model mimics the essential features of the interactions between amino acids, is consistent with the key paradigms of protein folding and reproduces the universal distribution of evolutionary rates among orthologous proteins. We introduce mean path divergence as a quantitative measure of the degree to which the starting and ending points determine the path of evolution in fitness landscapes. Global measures of landscape roughness are good predictors of path divergence in all studied landscapes: the mean path divergence is greater in smooth landscapes than in rough ones. The model-derived and experimental landscapes are significantly smoother than random landscapes and resemble additive landscapes perturbed with moderate amounts of noise; thus, these landscapes are substantially robust to mutation. The model landscapes show a deficit of suboptimal peaks even compared with noisy additive landscapes with similar overall roughness. We suggest that smoothness and the substantial deficit of peaks in the fitness landscapes of protein evolution are fundamental consequences of the physics of protein folding.Alexander E LobkovskyYuri I WolfEugene V KooninPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 12, p e1002302 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Alexander E Lobkovsky Yuri I Wolf Eugene V Koonin Predictability of evolutionary trajectories in fitness landscapes.
description	Experimental studies on enzyme evolution show that only a small fraction of all possible mutation trajectories are accessible to evolution. However, these experiments deal with individual enzymes and explore a tiny part of the fitness landscape. We report an exhaustive analysis of fitness landscapes constructed with an off-lattice model of protein folding where fitness is equated with robustness to misfolding. This model mimics the essential features of the interactions between amino acids, is consistent with the key paradigms of protein folding and reproduces the universal distribution of evolutionary rates among orthologous proteins. We introduce mean path divergence as a quantitative measure of the degree to which the starting and ending points determine the path of evolution in fitness landscapes. Global measures of landscape roughness are good predictors of path divergence in all studied landscapes: the mean path divergence is greater in smooth landscapes than in rough ones. The model-derived and experimental landscapes are significantly smoother than random landscapes and resemble additive landscapes perturbed with moderate amounts of noise; thus, these landscapes are substantially robust to mutation. The model landscapes show a deficit of suboptimal peaks even compared with noisy additive landscapes with similar overall roughness. We suggest that smoothness and the substantial deficit of peaks in the fitness landscapes of protein evolution are fundamental consequences of the physics of protein folding.
format	article
author	Alexander E Lobkovsky Yuri I Wolf Eugene V Koonin
author_facet	Alexander E Lobkovsky Yuri I Wolf Eugene V Koonin
author_sort	Alexander E Lobkovsky
title	Predictability of evolutionary trajectories in fitness landscapes.
title_short	Predictability of evolutionary trajectories in fitness landscapes.
title_full	Predictability of evolutionary trajectories in fitness landscapes.
title_fullStr	Predictability of evolutionary trajectories in fitness landscapes.
title_full_unstemmed	Predictability of evolutionary trajectories in fitness landscapes.
title_sort	predictability of evolutionary trajectories in fitness landscapes.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/4a480d0839954830b8e1433829248b14
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_version_	1718424723763757056

Predictability of evolutionary trajectories in fitness landscapes.

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